Transformation to AML and MDS in MPN patients over attained age and time since diagnosis: a nationwide cohort study

Supplementary material

1 Research objective

We aimed to model transformation rates simultaneously over attained age and time since diagnosis for MPN patients. Further subanalysis was carried out specific to cytoreductive treatment. Additional summary was also provided for genetic information.

2 Settings

Study design: cohort study

Data sources:

• the National Cancer Register (for MPN, other hematological malignancies)
• the National Inpatient Register (hospital admissions), the National Outpatient Register (specialist visits) (for MPN and AML/MDS diagnoses)
• the National Causes of Deaths Register (for AML/MDS diagnoses and other causes of deaths)
• the National Prescribed Drugs Register (for cytoreductive drugs)
• the Total Population Register (for demographic data)
• the MPN Quality Register (for genetic data)

Study period: three diagnosis-periods are covered in this study:

• 2001 - 2021: for describing transformation rates by subtypes
• 2006 - 2021: for summary by exposure to cytoreductive drugs
• 2008 - 2021: for summary by somatic mutations JAK2, CALR, MPL

Index date: at 3 months post MPN date date. Individuals were excluded if prior to the index date, they transformed to AML/MDS, developed other hematological malignancy, or they emigrated, or died.

Event of interest: transformation rates were analysed separately for AML and MDS. For the event AML, patients were not censored if they were diagnosed with MDS prior to AML, whereas for the MDS, patients were censored if they were diagnosed with AML prior to MDS.

End of follow-up: individuals were followed until the outcome of interest or censoring due to above, or due to emigration, due to death from other causes, or due to the end of follow-up (December 31, 2022); whichever occurred first.

3 Results

Cohort characteristics

Table 3.1: Characteristics of Swedish patients diagnosed during 2001-2021 with myeloproliferative neoplasms. PV=polycythemia vera, ET=essential thrombocythemia, PMF=primary myelofibrosis.

	PV (N=7156)	ET (N=6810)	PMF (N=1080)
Sex
Female	3178 (44.4%)	4200 (61.7%)	452 (41.9%)
Male	3978 (55.6%)	2610 (38.3%)	628 (58.1%)
Age at diagnosis
Median (Q1, Q3)	70.9 (60.8, 78.2)	68.2 (55.5, 77.3)	71.6 (62.7, 78.7)
18-49	684 (9.6%)	1192 (17.5%)	96 (8.9%)
50-59	1007 (14.1%)	1011 (14.8%)	114 (10.6%)
60-69	1718 (24.0%)	1504 (22.1%)	280 (25.9%)
70-79	2327 (32.5%)	1884 (27.7%)	370 (34.3%)
80-90	1420 (19.8%)	1219 (17.9%)	220 (20.4%)
Calendar period of diagnosis
2001-2010	3171 (44.3%)	2635 (38.7%)	325 (30.1%)
2011-2021	3985 (55.7%)	4175 (61.3%)	755 (69.9%)

Total person-years and events

Table 3.2: For each type of event of interest, number of individuals, person-years, number of events for patients by subtype, respectively.

Event		PV	ET	PMF
AML	N	7,156	6,810	1,080
	Person-years	51,234	50,546	5,132
	Events	190	191	135
MDS	N	7,156	6,810	1,080
	Person-years	50,842	50,131	4,980
	Events	115	166	83

Wald tests for parameters for t1 and t2

Main effects model

Fitted the following flexible parametric survival model on the log-hazard scale with two time-scales:

\[\begin{equation} \log h = s(t_1; \gamma_1) + s(t_2; \gamma_2) \tag{3.1} \end{equation}\]

where \(t_1\) is time since the index date and \(t_2\) is attained age. Functions \(s\) represent restricted cubic splines with \(\gamma\) as corresponding parameters.

Stata code for the model:

/* stset for reference time-scale: time since index date */
stset eof_date, origin(start_date) failure(status==1) scale(365.25) id(lopnr)

/* fit fpm model, where second time-scale, attained age, is defined by using start(start_age) */
stmt, ///
         time1(df(3) logtoff) ///
         time2(df(3) logtoff start(start_age))

Based on the above model, used Wald test for t1 and for t2 presented in the Table 3.3.

Table 3.3: P-vaues from the Wald tests for parameters of t1 and t2 from the model in (3.1).

	AML		MDS
	t1	t2	t1	t2
PV	0.01200	0.00167	0.06958	0.00089
ET	0.00350	0.00000	0.51549	0.00000
PMF	0.21794	0.33305	0.12399	0.02239

Model with interaction

Fitted the following flexible parametric survival model on the log-hazard scale with two time-scales:

\[\begin{equation} \log h = s(t_1; \gamma_1) + s(t_2; \gamma_2) + s(t_1, \gamma_3)\cdot(t_2, \gamma_4) \tag{3.2} \end{equation}\]

where \(t_1\) is time since the index date and \(t_2\) is attained age. Functions \(s\) represent restricted cubic splines with \(\gamma\) as corresponding parameters. Optimal number of knots for the spline functions were chosen based on AIC and BIC of the models.

Stata code for the model:

/* stset for reference time-scale: time since index date */
stset eof_date, origin(start_date) failure(status==1) scale(365.25) id(lopnr)

/* fit fpm model, where second time-scale, attained age, is defined by using start(start_age) */
stmt mpn, ///
         time1(df(3) logtoff) ///
         time2(df(3) logtoff start(start_age)) timeint(t1:t2 1:1)

Based on the above model, used Wald test for t1 and for t2 presented in the Table 3.4.

Table 3.4: P-vaues from the Wald tests for parameters of t1 and t2 from the model in (3.2).

	AML		MDS
	t1	t2	t1	t2
PV	0.02702	0.00427	0.07651	0.00179
ET	0.00790	0.00000	0.48399	0.00000
PMF	0.07767	0.09285	0.11817	0.03222

4 Transformation rates

AML

PV

Figure 4.1: Rates of transformation to AML per 1000 person-years in patients with polycythemia vera (PV). Panel (A): rates over attained age for PV patients with different ages at index date. Panel (B): rates for patients along time since index date with different ages at index date. Panel (C): rates over attained age for PV patients with different times since index date.

ET

Figure 4.2: Rates of transformation to AML per 1000 person-years in patients with essential thrombocythemia (ET). Panel (A): rates over attained age for ET patients with different ages at index date. Panel (B): rates for patients along time since index date with different ages at index date. Panel (C): rates over attained age for ET patients with different times since index date.

PMF

Figure 4.3: Rates of transformation to AML per 1000 person-years in patients with primary myelofibrosis (PMF). Panel (A): rates over attained age for PMF patients with different ages at index date. Panel (B): rates for patients along time since index date with different ages at index date. Panel (C): rates over attained age for PMF patients with different times since index date.

MDS

PV

Figure 4.4: Rates of transformation to MDS per 1000 person-years in patients with polycythemia vera (PV). Panel (A): rates over attained age for PV patients with different ages at index date. Panel (B): rates for patients along time since index date with different ages at index date. Panel (C): rates over attained age for PV patients with different times since index date.

ET

Figure 4.5: Rates of transformation to MDS per 1000 person-years in patients with essential thrombocythemia (ET). Panel (A): rates over attained age for ET patients with different ages at index date. Panel (B): rates for patients along time since index date with different ages at index date. Panel (C): rates over attained age for ET patients with different times since index date.

PMF

Figure 4.6: Rates of transformation to MDS per 1000 person-years in patients with primary myelofibrosis (PMF). Panel (A): rates over attained age for PMF patients with different ages at index date. Panel (B): rates for patients along time since index date with different ages at index date. Panel (C): rates over attained age for PMF patients with different times since index date.

Data

Below are few selected rows from the dataset containing estimated rates of transformation for MPN subtypes with outcomes AML and MDS. If you wish to see full data (size ~38 Mb), the following download link will provide the excel file: https://github.com/nurbatyr/Suppl-material-transformation-rates-in-MPN-over-2ts/releases/download/transform-rates-main/021_estimated_transform_rates.xlsx

subtype	event	age_start	attained_age	fu_time	h	h_lci	h_uci
PV	AML	65	65.00	0.00	2.859	1.687	4.845
PV	AML	65	65.05	0.05	2.870	1.708	4.822
PV	AML	65	65.10	0.10	2.881	1.729	4.800
PV	AML	65	65.15	0.15	2.892	1.750	4.779
PV	AML	65	65.20	0.20	2.903	1.771	4.758
PV	AML	65	65.25	0.25	2.914	1.793	4.737
PV	AML	65	65.30	0.30	2.925	1.814	4.717
PV	AML	65	65.35	0.35	2.936	1.835	4.698
PV	AML	65	65.40	0.40	2.947	1.856	4.679
PV	AML	65	65.45	0.45	2.958	1.878	4.661

Stata code

/* create a dataset with time1, time2 and age0 */
local n=1
local datalist ""
forvalues a0= 30 30.25 : 90 {
    qui clear
    qui set obs 401 
    qui gen age_start =`a0'             // a0: age at index
    qui range time1 0 20 401            // t1: time on study
    qui gen time2 = time1 + age_start   // t2: attained age
    qui gen _d = .
    qui gen _t = .
    
    /* predict hazard rates */
    qui predict h, h time1var(time1) time2var(time2) per(1000) ci
    
    qui tempfile temppred`n'
    qui save `temppred`n''
    local datalist `datalist' `temppred`n''
    local n=`n'+1
}

qui clear 
qui set obs 0 
qui append using `datalist' 
qui order age_start time2
    
keep time1 time2 age_start h*

5 Cumulative incidence function for 5y, 10y, 15y for ages at index date

Figure

Figure 5.1: Cumulative incidence function at 5-, 10-, and 15-years since the index date by subtypes for transformation to AML and to MDS for different ages at index date, respectively, (index date = MPN diagnosis date + 3m). Panel (A) accounting for death from other causes as a competing risk. Panel (B) accounting for transformation to AML or death from other causes as competing risks. PV = polycythemia vera, ET = essential thrombocythemia, PMF = primary myelofibrosis

Table

Table 5.1: Cumulative incidence function (CIF) with 95% confidence intervals (CI) at 5-, 10-, and 15-years since the index date by subtypes for transformation to AML and to MDS for different ages at index date, respectively, (index date = MPN diagnosis date + 3m). For AML event considered death from other causes as a competing risk. For MDS event considered transformation to AML or death from other causes as competing risks. PV = polycythemia vera, ET = essential thrombocythemia, PMF = primary myelofibrosis.

Event	Age at index date	Time since index date	PV, CIF (95% CI)	ET, CIF (95% CI)	PMF, CIF (95% CI)
AML	55	5y	0.9% (0.5%, 1.3%)	0.6% (0.3%, 0.8%)	12.2% (8.3%, 16.0%)
		10y	2.6% (1.9%, 3.3%)	2.2% (1.6%, 2.9%)	17.3% (12.1%, 22.6%)
		15y	5.0% (3.7%, 6.2%)	4.8% (3.5%, 6.2%)	20.8% (14.3%, 27.3%)
	60	5y	1.3% (0.9%, 1.7%)	1.1% (0.7%, 1.5%)	12.9% (9.1%, 16.7%)
		10y	3.4% (2.6%, 4.2%)	3.8% (2.8%, 4.7%)	18.7% (13.8%, 23.7%)
		15y	5.7% (4.5%, 6.9%)	6.6% (5.2%, 8.1%)	22.6% (16.9%, 28.4%)
	65	5y	1.6% (1.2%, 2.1%)	1.8% (1.3%, 2.3%)	13.1% (9.9%, 16.3%)
		10y	3.7% (2.9%, 4.5%)	4.8% (3.8%, 5.9%)	19.1% (15.0%, 23.2%)
		15y	5.6% (4.6%, 6.7%)	7.2% (5.8%, 8.5%)	23.0% (18.0%, 27.9%)
	70	5y	1.7% (1.3%, 2.1%)	2.1% (1.6%, 2.7%)	12.6% (9.9%, 15.3%)
		10y	3.5% (2.8%, 4.1%)	4.7% (3.9%, 5.6%)	18.2% (14.3%, 22.0%)
		15y	4.9% (3.9%, 5.8%)	6.5% (5.2%, 7.7%)	21.3% (16.6%, 26.0%)
	75	5y	1.5% (1.2%, 1.9%)	1.9% (1.5%, 2.4%)	11.4% (8.5%, 14.3%)
		10y	2.9% (2.3%, 3.6%)	4.0% (3.2%, 4.9%)	15.9% (12.1%, 19.7%)
	80	5y	1.3% (1.0%, 1.7%)	1.7% (1.2%, 2.2%)	9.7% (7.0%, 12.3%)
MDS	55	5y	0.7% (0.4%, 1.0%)	0.9% (0.6%, 1.2%)	4.0% (1.9%, 6.0%)
		10y	1.4% (0.9%, 1.9%)	1.6% (1.1%, 2.1%)	7.6% (4.3%, 11.0%)
		15y	2.2% (1.5%, 3.0%)	2.4% (1.7%, 3.2%)	10.7% (6.0%, 15.4%)
	60	5y	0.8% (0.5%, 1.1%)	1.0% (0.7%, 1.4%)	4.9% (2.7%, 7.1%)
		10y	1.5% (1.0%, 2.0%)	2.0% (1.4%, 2.5%)	8.7% (5.3%, 12.1%)
		15y	2.4% (1.7%, 3.1%)	3.1% (2.2%, 3.9%)	11.2% (7.0%, 15.4%)
	65	5y	0.9% (0.5%, 1.2%)	1.3% (0.9%, 1.6%)	6.1% (3.8%, 8.3%)
		10y	1.6% (1.1%, 2.1%)	2.4% (1.8%, 3.1%)	9.8% (6.7%, 12.9%)
		15y	2.5% (1.9%, 3.2%)	3.8% (3.0%, 4.7%)	11.6% (7.9%, 15.2%)
	70	5y	0.9% (0.6%, 1.2%)	1.6% (1.2%, 2.0%)	7.4% (5.3%, 9.5%)
		10y	1.7% (1.2%, 2.1%)	3.1% (2.4%, 3.7%)	10.7% (7.8%, 13.6%)
		15y	2.6% (1.9%, 3.2%)	4.6% (3.6%, 5.7%)	11.7% (8.4%, 14.9%)
	75	5y	0.9% (0.7%, 1.2%)	2.0% (1.6%, 2.5%)	8.5% (5.9%, 11.1%)
		10y	1.7% (1.2%, 2.1%)	3.7% (2.9%, 4.5%)	10.8% (7.7%, 14.0%)
	80	5y	1.0% (0.6%, 1.3%)	2.4% (1.8%, 3.1%)	8.6% (5.9%, 11.3%)

Stata code

/* For event AML */

* FPM on log-hazard scale with competing events (AML and death from other causes) over time-scales:
* t1 = time since index date
* t2 = attained age
* for AML: log h=s(t1, df=3) + s(t2, df=3) + t1*t2
* for Death: log h=s(t2, df=4)
// ssc install merlin
merlin (time_y /// /* AML */
        rcs(time_y, df(3) orthog event) ///
        rcs(time_y, offset(start_age) df(3) orthog event) ///
        rcs(time_y, df(1) orthog event)#rcs(time_y, offset(start_age) df(1) orthog event), ///
        family(loghazard, failure(cause_aml)) ///
        timevar(time_y)) ///
        (time_y /// /* Death from other causes */
        rcs(time_y, offset(start_age) df(4) orthog event), ///
        family(loghazard, failure(cause_death)) ///
        timevar(time_y)) 

/* estimate cumulative incidence function over two time-scales */
local n=1
local datalist ""
forvalues a0= 55 60 : 80 {
    qui clear
    qui set obs 401
    qui gen start_age =`a0'             // a0: age at index
    qui range time1 0 20 401            // t1: time on study
    qui gen time2 = time1 + start_age   // t2: attained age
    qui gen time_y = .
    qui gen cause_aml = .
    qui gen cause_death = .
    
    predict cif_aml, cif outcome(1) causes(1 2) ci timevar(time1) at(start_age `a0')
    predict cif_death, cif outcome(2) causes(1 2) ci timevar(time1) at(start_age `a0')
    
    qui tempfile temppred`n'
    qui save `temppred`n''
    local datalist `datalist' `temppred`n''
    local n=`n'+1
}

qui clear 
qui set obs 0 
qui append using `datalist' 
keep time1 time2 start_age cif*

6 By cytoreductive treatment

We investigated further the patterns of transformation rates over two time-scales in MPN patients overall by exposure to cytoreductive treatments.

The cytoreductive treatment constituted any of the following drugs that were collected after the MPN date and before the end of follow-up: interferon (ATC: L03AB11, L03AB10), hydroxyurea (ATC: L01XX05), ruxolitinib (ATC: L01EJ01), anagrelide (ATC: L01XX35), busulfan (ATC: L01AB01).

Since the Prescribed Drugs Register was established in July 2005, we looked at MPN patients diagnosed during 2006-2021. A time-varying treatment variable was created based on the following criteria:

• “0” = Not initiated cytoreductive treatment,
• “1” = Initiated cytoreductive treatment.

Patients can go from treatment state 0 to 1, or start at 1. Note: for the analysis, only treatment states that were at index date and beyond were included.

Person-years and event numbers by drugs

Due to very few events for specific drugs, it was not possible to fit a model by exposure to different drugs. Below is the summary of person-years and number of events by outcome for each sutbype.

AML

Table 6.1: MPN-patients with diagnosis during 2006-2021 at different treatment states after index date (3 months post MPN-diagnosis) during the follow-up where the outcome is AML. Treatment state is a time-varying covariate, so patients can go from 0 to 1, from 0 to 2, from 1 to 3, from 2 to 3, and reach state 4 from any state, where ‘0’ = No cytoreductive treatment, ‘1’ = 2 collections of Interferon, ‘2’ = 2 collections of Hydroxyurea, ‘3’ = 2 collections of each Interferon and Hydroxyurea, ‘4’ = 2 collections of Jakavi or Anagrelide, or 1 collection of Busulfan (with possible previous treatment states 0-3). Note: starting treatment state can be any of the above.

Subtype	Treatment state	Person-years	Number of events	N
PV	0 = No cytoreductive treatment	15,557	15	4,618
	1 = 2 collections of Interferon	1,366	1	300
	2 = 2 collections of Hydroxyurea	15,386	72	2,994
	3 = 2 collections of each Interferon and Hydroxyurea	860	5	203
	4 = 2 collections of Jakavi or Anagrelide, or 1 collection of Busulfan (with possible previous treatment states 0-3).	1,364	25	386
ET	0 = No cytoreductive treatment	12,667	9	4,255
	1 = 2 collections of Interferon	1,519	3	322
	2 = 2 collections of Hydroxyurea	17,204	68	3,263
	3 = 2 collections of each Interferon and Hydroxyurea	773	3	180
	4 = 2 collections of Jakavi or Anagrelide, or 1 collection of Busulfan (with possible previous treatment states 0-3).	2,747	23	585
PMF	0 = No cytoreductive treatment	1,618	45	757
	1 = 2 collections of Interferon	253	0	65
	2 = 2 collections of Hydroxyurea	1,771	41	481
	3 = 2 collections of each Interferon and Hydroxyurea	125	2	34
	4 = 2 collections of Jakavi or Anagrelide, or 1 collection of Busulfan (with possible previous treatment states 0-3).	586	28	218
Total number of patients in the cohort N=12043.

MDS

Table 6.2: MPN-patients with diagnosis during 2006-2021 at different treatment states after index date (3 months post MPN-diagnosis) during the follow-up where the outcome is MDS. Treatment state is a time-varying covariate, so patients can go from 0 to 1, from 0 to 2, from 1 to 3, from 2 to 3, and reach state 4 from any state, where ‘0’ = No cytoreductive treatment, ‘1’ = 2 collections of Interferon, ‘2’ = 2 collections of Hydroxyurea, ‘3’ = 2 collections of each Interferon and Hydroxyurea, ‘4’ = 2 collections of Jakavi or Anagrelide, or 1 collection of Busulfan (with possible previous treatment states 0-3). Note: starting treatment state can be any of the above.

Subtype	Treatment state	Person-years	Number of events	N
PV	0 = No cytoreductive treatment	15,511	12	4,618
	1 = 2 collections of Interferon	1,361	1	299
	2 = 2 collections of Hydroxyurea	15,289	43	2,991
	3 = 2 collections of each Interferon and Hydroxyurea	858	3	202
	4 = 2 collections of Jakavi or Anagrelide, or 1 collection of Busulfan (with possible previous treatment states 0-3).	1,330	9	380
ET	0 = No cytoreductive treatment	12,617	26	4,255
	1 = 2 collections of Interferon	1,516	2	322
	2 = 2 collections of Hydroxyurea	17,100	61	3,256
	3 = 2 collections of each Interferon and Hydroxyurea	772	2	180
	4 = 2 collections of Jakavi or Anagrelide, or 1 collection of Busulfan (with possible previous treatment states 0-3).	2,711	18	581
PMF	0 = No cytoreductive treatment	1,542	37	757
	1 = 2 collections of Interferon	250	2	65
	2 = 2 collections of Hydroxyurea	1,736	24	476
	3 = 2 collections of each Interferon and Hydroxyurea	125	0	34
	4 = 2 collections of Jakavi or Anagrelide, or 1 collection of Busulfan (with possible previous treatment states 0-3).	561	11	210
Total number of patients in the cohort N=12043.

Person-years and event numbers by initiation

Due to very few events for state “Not initiated cytoreductive treatment”, it was not possible to fit a model. Below is the summary of person-years and number of events by outcome for each sutbype.

AML

Table 6.3: MPN-patients with diagnosis during 2006-2021 at different treatment states after index date (3 months post MPN-diagnosis) during the follow-up where the outcome is AML. Treatment state is a time-varying covariate, so patients can go from 0 to 1, or start at 1, where ‘0’ = Not initiated cytoreductive treatment, ‘1’ = Initiated cytoreductive treatment. Note: starting treatment state can be any of the above.

Subtype	Treatment state	Person-years	Number of events	N
PV	0 = Not initiated cytoreductive treatment	14,581	13	3,327
	1 = Initiated cytoreductive treatment	19,951	105	3,515
ET	0 = Not initiated cytoreductive treatment	11,720	5	2,631
	1 = Initiated cytoreductive treatment	23,191	101	3,941
PMF	0 = Not initiated cytoreductive treatment	1,450	39	491
	1 = Initiated cytoreductive treatment	2,903	77	699
Total number of patients in the cohort N=12043.

MDS

Table 6.4: MPN-patients with diagnosis during 2006-2021 at different treatment states after index date (3 months post MPN-diagnosis) during the follow-up where the outcome is MDS. Treatment state is a time-varying covariate, so patients can go from 0 to 1, or start at 1, where ‘0’ = Not initiated cytoreductive treatment, ‘1’ = Initiated cytoreductive treatment. Note: starting treatment state can be any of the above.

Subtype	Treatment state	Person-years	Number of events	N
PV	0 = Not initiated cytoreductive treatment	14,537	12	3,327
	1 = Initiated cytoreductive treatment	19,812	56	3,511
ET	0 = Not initiated cytoreductive treatment	11,671	20	2,631
	1 = Initiated cytoreductive treatment	23,045	89	3,937
PMF	0 = Not initiated cytoreductive treatment	1,378	28	491
	1 = Initiated cytoreductive treatment	2,835	46	694
Total number of patients in the cohort N=12043.

7 Somatic mutations

Information on driver gene mutations was obtained from the Swedish MPN Quality Register, which was established in 2008. Due to the sparsity and potential unreliability of data, it was not possible to fit any models. Here we provide only summary statistics for JAK2, CALR and MPL mutations.

Characteristics

PV

Table 7.1: Characteristics of patients diagnosed during 2008-2021 with Polycythemia Vera (PV) by somatic mutation status.

	JAK2			CALR		MPL		JAK2, CALR, MPL
	Pos	Neg	Missing	Pos	Neg/Missing	Pos	Neg/Missing	Triple positive
N (% among PV)	2213 (95.2%)	32 (1.4%)	80 (3.4%)	4 (0.2%)	2321 (99.8%)	1 (0.0%)	2324 (100.0%)	0
Age at diagnosis
Median (Q1, Q3)	70.2 (61.0, 76.8)	69.8 (55.0, 74.9)	70.8 (60.6, 78.3)	48.1 (35.8, 61.5)	70.2 (60.9, 76.8)	62.5 (62.5, 62.5)	70.2 (60.9, 76.8)
18-49	207 (9.4%)	7 (21.9%)	13 (16.2%)	2 (50.0%)	225 (9.7%)	0 (0.0%)	227 (9.8%)
50-59	313 (14.1%)	4 (12.5%)	6 (7.5%)	1 (25.0%)	322 (13.9%)	0 (0.0%)	323 (13.9%)
60-69	570 (25.8%)	5 (15.6%)	18 (22.5%)	0 (0.0%)	593 (25.5%)	1 (100.0%)	592 (25.5%)
70-79	771 (34.8%)	10 (31.2%)	25 (31.2%)	1 (25.0%)	805 (34.7%)	0 (0.0%)	806 (34.7%)
80-90	352 (15.9%)	6 (18.8%)	18 (22.5%)	0 (0.0%)	376 (16.2%)	0 (0.0%)	376 (16.2%)
Sex
Female	1110 (50.2%)	14 (43.8%)	44 (55.0%)	2 (50.0%)	1166 (50.2%)	1 (100.0%)	1167 (50.2%)
Male	1103 (49.8%)	18 (56.2%)	36 (45.0%)	2 (50.0%)	1155 (49.8%)	0 (0.0%)	1157 (49.8%)
Calendar period of diagnosis
2008-2015	1155 (52.2%)	5 (15.6%)	57 (71.2%)	0 (0.0%)	1217 (52.4%)	1 (100.0%)	1216 (52.3%)
2016-2021	1058 (47.8%)	27 (84.4%)	23 (28.8%)	4 (100.0%)	1104 (47.6%)	0 (0.0%)	1108 (47.7%)

ET

Table 7.2: Characteristics of patients diagnosed during 2008-2021 with Essential Thrombocythemia (ET) by somatic mutation status.

	JAK2			CALR		MPL		JAK2, CALR, MPL
	Pos	Neg	Missing	Pos	Neg/Missing	Pos	Neg/Missing	Triple positive
N (% among ET)	1823 (63.5%)	546 (19.0%)	502 (17.5%)	249 (8.7%)	2622 (91.3%)	71 (2.5%)	2800 (97.5%)	4 (100.0%)
Age at diagnosis
Median (Q1, Q3)	69.0 (58.3, 76.4)	67.1 (54.5, 75.7)	68.5 (54.9, 78.4)	67.0 (54.5, 75.8)	68.8 (57.1, 76.7)	69.4 (56.0, 75.9)	68.6 (56.9, 76.7)	61.3 (54.4, 69.9)
18-49	264 (14.5%)	107 (19.6%)	96 (19.1%)	48 (19.3%)	419 (16.0%)	11 (15.5%)	456 (16.3%)	0 (0.0%)
50-59	249 (13.7%)	85 (15.6%)	71 (14.1%)	45 (18.1%)	360 (13.7%)	11 (15.5%)	394 (14.1%)	2 (50.0%)
60-69	450 (24.7%)	120 (22.0%)	98 (19.5%)	49 (19.7%)	619 (23.6%)	16 (22.5%)	652 (23.3%)	1 (25.0%)
70-79	572 (31.4%)	158 (28.9%)	132 (26.3%)	75 (30.1%)	787 (30.0%)	25 (35.2%)	837 (29.9%)	1 (25.0%)
80-90	288 (15.8%)	76 (13.9%)	105 (20.9%)	32 (12.9%)	437 (16.7%)	8 (11.3%)	461 (16.5%)	0 (0.0%)
Sex
Female	1110 (60.9%)	290 (53.1%)	273 (54.4%)	118 (47.4%)	1555 (59.3%)	41 (57.7%)	1632 (58.3%)	2 (50.0%)
Male	713 (39.1%)	256 (46.9%)	229 (45.6%)	131 (52.6%)	1067 (40.7%)	30 (42.3%)	1168 (41.7%)	2 (50.0%)
Calendar period of diagnosis
2008-2015	871 (47.8%)	142 (26.0%)	445 (88.6%)	35 (14.1%)	1423 (54.3%)	7 (9.9%)	1451 (51.8%)	0 (0.0%)
2016-2021	952 (52.2%)	404 (74.0%)	57 (11.4%)	214 (85.9%)	1199 (45.7%)	64 (90.1%)	1349 (48.2%)	4 (100.0%)

PMF

Table 7.3: Characteristics of patients diagnosed during 2008-2021 with Primary Myelofibrosis (PMF) by somatic mutation status.

	JAK2			CALR		MPL		JAK2, CALR, MPL
	Pos	Neg	Missing	Pos	Neg/Missing	Pos	Neg/Missing	Triple positive
N (% among PMF)	440 (54.3%)	200 (24.7%)	170 (21.0%)	96 (11.9%)	714 (88.1%)	25 (3.1%)	785 (96.9%)	0
Age at diagnosis
Median (Q1, Q3)	72.8 (65.1, 79.1)	70.1 (58.5, 76.8)	70.0 (61.8, 77.4)	65.1 (55.4, 75.4)	72.2 (63.9, 78.8)	73.2 (65.7, 78.4)	71.6 (62.8, 78.5)
18-49	29 (6.6%)	23 (11.5%)	18 (10.6%)	14 (14.6%)	56 (7.8%)	1 (4.0%)	69 (8.8%)
50-59	32 (7.3%)	33 (16.5%)	19 (11.2%)	19 (19.8%)	65 (9.1%)	5 (20.0%)	79 (10.1%)
60-69	114 (25.9%)	43 (21.5%)	48 (28.2%)	23 (24.0%)	182 (25.5%)	4 (16.0%)	201 (25.6%)
70-79	167 (38.0%)	68 (34.0%)	57 (33.5%)	30 (31.2%)	262 (36.7%)	10 (40.0%)	282 (35.9%)
80-90	98 (22.3%)	33 (16.5%)	28 (16.5%)	10 (10.4%)	149 (20.9%)	5 (20.0%)	154 (19.6%)
Sex
Female	185 (42.0%)	93 (46.5%)	69 (40.6%)	41 (42.7%)	306 (42.9%)	15 (60.0%)	332 (42.3%)
Male	255 (58.0%)	107 (53.5%)	101 (59.4%)	55 (57.3%)	408 (57.1%)	10 (40.0%)	453 (57.7%)
Calendar period of diagnosis
2008-2015	204 (46.4%)	47 (23.5%)	149 (87.6%)	10 (10.4%)	390 (54.6%)	3 (12.0%)	397 (50.6%)
2016-2021	236 (53.6%)	153 (76.5%)	21 (12.4%)	86 (89.6%)	324 (45.4%)	22 (88.0%)	388 (49.4%)

8 Statistical Software

The statistical software used for the analysis: Stata 18.0 and R version 4.3.1.